KRIGING INTERPOLATION¶
Fits a Kriging (Gaussian Process) surrogate model to a training dataset and uses it to interpolate predictions over a new set of input points. Also identifies the optimum (minimum or maximum) of the response surface. Use this worker when you need a smooth, probabilistic surrogate for design-space exploration or optimum-seeking tasks.
When to use¶
Tagged: design exploration, gaussian process, interpolation, kriging, optimization, response surface, surrogate.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Dataset | dataset | dataset | — | ✓ | Training dataset containing independent variable columns and at least one target/response column; must be a structured tabular dataset with no missing values. |
| Independents | independents | select | — | ✓ | Column name(s) from the training dataset to treat as independent (input) variables for the Kriging model. |
| Targets | targets | select | — | ✓ | Column name(s) from the training dataset to treat as response (output) variables that the Kriging model will learn to predict. |
| Objective | objective | select | min | ✓ | Optimization direction for identifying the optimum on the fitted response surface; choose ‘min’ to minimize or ‘max’ to maximize the target — defaults to ‘min’. |
| Predict For | predict_dataset | dataset | — | Optional dataset of new input points (must share the same column names as the selected independents) for which the fitted Kriging model will generate predictions; leave empty to skip batch prediction. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| Input Dataset | dataset | dataset | The original training dataset passed through, augmented with Kriging fitted values for diagnostic or downstream use. |
| Predictions | predictions | dataset | Tabular dataset of Kriging-predicted response values for each row in the ‘Predict For’ input dataset; columns correspond to the selected target variables. |
| Optimum | optimum | dataset | Single-row dataset identifying the input-variable combination and predicted response value that achieves the requested optimum (minimum or maximum) on the surrogate surface. |
Disciplines¶
- ai_ml.surrogate
- design_exploration.doe
- design_exploration.optimization
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